auto dict_attr =
          func->getAttrOfType<mlir::DictionaryAttr>("tf.entry_function");
      if (!dict_attr) continue;

      OpBuilder builder(func.getContext());
      builder.setInsertionPointToStart(&func.getBlocks().front());
      builder.create<TFL::CallOnceOp>(func.getLoc(), init_func_op.getName());
    }
  }
}

}  // namespace

func::FuncOp BuildGELUDecomposition(RankedTensorType type,
                                    PatternRewriter& rewriter,
                                    Block* insertion_point) {
  rewriter.setInsertionPointToStart(insertion_point);

  auto ftype = FunctionType::get(rewriter.getContext(), {type}, {type});
  auto name = rewriter.getStringAttr("gelu_decomp");
  func::FuncOp new_func = rewriter.create<func::FuncOp>(

    module_ = builder.create<mlir::ModuleOp>(builder.getUnknownLoc());

    builder.setInsertionPointToStart(module_->getBody());
    auto func = builder.create<mlir::func::FuncOp>(  //
        builder.getUnknownLoc(), "main", builder.getFunctionType({}, {}));
    func->setAttr("tfl.func", builder.getUnitAttr());
    builder.setInsertionPointToStart(func.addEntryBlock());
    llvm::SmallVector<int> shape{1, 2, 3, 4, 5, 6, 7, 8, 9, 10};

  // Make savedmodel verification happy.
  init->setAttr("tf_saved_model.exported_names",
                builder.getStrArrayAttr({name}));

  builder.setInsertionPointToStart(init.addEntryBlock());

  for (func::FuncOp func : tf_saved_model::GetInitializerFunctions(module)) {
    if (func.getNumArguments() != 0) {
      session_initializer->emitWarning(

          continue;
        }
      }
      if (arg_is_deletable) {
        args_to_erase.set(i);
      }

      // Replace the arg with a tf.Const op in the function body.
      builder.setInsertionPointToStart(&func.getBody().front());

      std::string asset_filename = asset.getFilename().str();
      std::string filename =
          tensorflow::io::JoinPath(saved_model_dir, asset_filename);

    FunctionType func_type = rewriter.getFunctionType({}, {});
    func::FuncOp func = rewriter.create<func::FuncOp>(
        loc, kImportModelDefaultGraphFuncName, func_type);
    rewriter.setInsertionPointToStart(func.addEntryBlock());
    auto executor_graph =
        rewriter.create<tf_executor::GraphOp>(loc, func_type.getResults());
    rewriter.inlineRegionBefore(graph.getNodes(), executor_graph.getBody(),

      tensorflow::ConvertTensor(*tensor, &builder);
  assert(tensor_attr_or.ok() && "Expect valid tensor");
  ElementsAttr tensor_attr = tensor_attr_or.value();

  builder.setInsertionPointToStart(&session_init_func.getBlocks().front());
  auto var_handle_op_in_init = var_handle_op->clone();
  builder.insert(var_handle_op_in_init);
  auto const_op = builder.create<mlir::arith::ConstantOp>(

    func::FuncOp func, llvm::SmallVectorImpl<Type>& new_input_types,
    OpBuilder builder) {
  if (mlir::isa<FloatType>(input_type)) {
    return success();
  }

  Block& block = func.front();
  builder.setInsertionPointToStart(&block);

  for (int i = 0; i != block.getNumArguments(); ++i) {
    Value arg = block.getArgument(0);
    Type arg_type = arg.getType();
    Value new_arg = arg;
    Location loc = func.getLoc();

          for (OpOperand &use : user->getOpOperands())
            bvector.set(use.getOperandNumber());
        }
      }

      // Replace the arg with a tf.Const op in the function body.
      builder.setInsertionPointToStart(&func.getBody().front());
      auto const_op = builder.create<TF::ConstOp>(global_tensor.getLoc(),
                                                  *global_tensor.getValue());

  Block &block = value.getParentRegion()->front();
  Operation *op = value.getDefiningOp();
  if (op) {
    builder.setInsertionPoint(&block, ++Block::iterator(op));
  } else {
    builder.setInsertionPointToStart(&block);
  }
  TypeAttr type_attr = TypeAttr::get(new_type);
  auto quantize = builder.create<TFL::QuantizeOp>(value.getLoc(), new_type,
                                                  value, type_attr);